DROIDSWAN: Detecting Malicious Android Applications Based on Static Feature A...csandit
Android being a widely used mobile platform has witnessed an increase in the number of malicious samples on its market place. The availability of multiple sources for downloading
applications has also contributed to users falling prey to malicious applications. Classification of an Android application as malicious or benign remains a challenge as malicious applications maneuver to pose themselves as benign. This paper presents an approach which extracts various features from Android Application Package file (APK) using static analysis and subsequently classifies using machine learning techniques. The contribution of this work includes deriving, extracting and analyzing crucial features of Android applications that aid in efficient classification. The analysis is carried out using various machine learning algorithms
with both weighted and non-weighted approaches. It was observed that weighted approach depicts higher detection rates using fewer features. Random Forest algorithm exhibited high detection rate and shows the least false positive rate.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a
software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152
malware samples belonging to 16 families and 523 benign samples.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples.
Today’s threats have become very complex and serious in their packing and encryption techniques. Every day new malware variants are becoming increasingly in quantity together with quality by using packing and encrypting techniques. The challenges in this research field are the traditional malware detection systems sometimes might fail to detect new malware variants and produces false alarms. Malicious software in the form of virus, worm, trojan, ransom, and spy harms our computer systems, network environment, and organizations in various ways. Therefore, malware analysis for detection and family classification plays a significant role in Cyber Crime Incident Handling Systems. This system contributes malware family classification with 10 prominent features by conduction feature selection process. The process of labeling the malicious samples using Regular Expressions has been contributed in this approach. The proposed malware classification system provides 7 different families including malware and benign using machine learning classifiers. The finding from our experiment proves that the selected 10 API features provide the best evaluation metrics in terms of accuracy, precision-recall, and ROC scores.
Android is an extensively used mobile platform and with evolution it has also witnessed an increased influx of malicious applications in its market place. The availability of multiple sources for downloading applications has also contributed to users falling prey to malicious applications. A major hindrance in blocking the entry of malicious applications into the Android market place is scarcity of effective mechanisms to identify malicious applications. This paper presents AndroInspector, a system for comprehensive analysis of an Android application using both static and dynamic analysis techniques. And roInspector derives, extracts and analyses crucial features of Android applications using static analysis and subsequently classifies the application using machine learning techniques. Dynamic analysis includes automated execution of Android application to identify a set of pre-defined malicious actions performed by application at run-time.
DROIDSWAN: Detecting Malicious Android Applications Based on Static Feature A...csandit
Android being a widely used mobile platform has witnessed an increase in the number of malicious samples on its market place. The availability of multiple sources for downloading
applications has also contributed to users falling prey to malicious applications. Classification of an Android application as malicious or benign remains a challenge as malicious applications maneuver to pose themselves as benign. This paper presents an approach which extracts various features from Android Application Package file (APK) using static analysis and subsequently classifies using machine learning techniques. The contribution of this work includes deriving, extracting and analyzing crucial features of Android applications that aid in efficient classification. The analysis is carried out using various machine learning algorithms
with both weighted and non-weighted approaches. It was observed that weighted approach depicts higher detection rates using fewer features. Random Forest algorithm exhibited high detection rate and shows the least false positive rate.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a
software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152
malware samples belonging to 16 families and 523 benign samples.
MINING PATTERNS OF SEQUENTIAL MALICIOUS APIS TO DETECT MALWAREIJNSA Journal
In the era of information technology and connected world, detecting malware has been a major security concern for individuals, companies and even for states. The New generation of malware samples upgraded with advanced protection mechanism such as packing, and obfuscation frustrate anti-virus solutions. API call analysis is used to identify suspicious malicious behavior thanks to its description capability of a software functionality. In this paper, we propose an effective and efficient malware detection method that uses sequential pattern mining algorithm to discover representative and discriminative API call patterns. Then, we apply three machine learning algorithms to classify malware samples. Based on the experimental results, the proposed method assures favorable results with 0.999 F-measure on a dataset including 8152 malware samples belonging to 16 families and 523 benign samples.
Today’s threats have become very complex and serious in their packing and encryption techniques. Every day new malware variants are becoming increasingly in quantity together with quality by using packing and encrypting techniques. The challenges in this research field are the traditional malware detection systems sometimes might fail to detect new malware variants and produces false alarms. Malicious software in the form of virus, worm, trojan, ransom, and spy harms our computer systems, network environment, and organizations in various ways. Therefore, malware analysis for detection and family classification plays a significant role in Cyber Crime Incident Handling Systems. This system contributes malware family classification with 10 prominent features by conduction feature selection process. The process of labeling the malicious samples using Regular Expressions has been contributed in this approach. The proposed malware classification system provides 7 different families including malware and benign using machine learning classifiers. The finding from our experiment proves that the selected 10 API features provide the best evaluation metrics in terms of accuracy, precision-recall, and ROC scores.
Android is an extensively used mobile platform and with evolution it has also witnessed an increased influx of malicious applications in its market place. The availability of multiple sources for downloading applications has also contributed to users falling prey to malicious applications. A major hindrance in blocking the entry of malicious applications into the Android market place is scarcity of effective mechanisms to identify malicious applications. This paper presents AndroInspector, a system for comprehensive analysis of an Android application using both static and dynamic analysis techniques. And roInspector derives, extracts and analyses crucial features of Android applications using static analysis and subsequently classifies the application using machine learning techniques. Dynamic analysis includes automated execution of Android application to identify a set of pre-defined malicious actions performed by application at run-time.
MALWARE DETECTION USING MACHINE LEARNING ALGORITHMS AND REVERSE ENGINEERING O...IJNSA Journal
This research paper is focused on the issue of mobile application malware detection by Reverse Engineering of Android java code and use of Machine Learning algorithms. The malicious software characteristics were identified based on a collected set of total number of 1958 applications (including 996 malware applications). During research a unique set of features was chosen, then three attribute selection algorithms and five classification algorithms (Random Forest, K Nearest Neighbors, SVM, Nave Bayes and Logistic Regression) were examined to choose algorithms that would provide the most effective rate of malware detection.
Android Malware Detection in Official and Third Party Application StoresEswar Publications
Android is one of the most popular operating system for mobile devices and tablets. The growing number of Android users and open source nature of this platform has also attracted attackers to target Android devices. This paper presents the static and dynamic analysis of the Android applications in order to detect malware. In this work, we have performed permission based and behavioural based filtering of Android applications with the help of malware analysis tools. Our results revel that 80% of the applications request for dangerous permissions.
13% applications consist of malicious activities. Most of the applications are interested in the device data like contact lists, IMEI, IMSI, SMS etc. These results clearly indicate the need for better security measures for Android apps.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis
approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially
overcome these deceits by observing the actual behaviour of the code execution. In this regard, various
methods, techniques and tools have been proposed. However, because of the diverse concepts and
strategies used in the implementation of these methods and tools, security researchers and malware
analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to
contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call
monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic
malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s
implementation strategy, analysis approach, system-wide analysis support and its overall handling of
binaries, helping them to select a suitable and effective one for their study and analysis.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
an error in that computer program. In order to improve the software quality, prediction of faulty modules is
necessary. Various Metric suites and techniques are available to predict the modules which are critical and
likely to be fault prone. Genetic Algorithm is a problem solving algorithm. It uses genetics as its model of
problem solving. It’s a search technique to find approximate solutions to optimization and search
problems.Genetic algorithm is applied for solving the problem of faulty module prediction and as well as
for finding the most important attribute for fault occurrence. In order to perform the analysis, performance
validation of the Genetic Algorithm using open source software jEdit is done. The results are measured in
terms Accuracy and Error in predicting by calculating probability of detection and probability of false
Alarms
DETECTION OF MALICIOUS EXECUTABLES USING RULE BASED CLASSIFICATION ALGORITHMSAAKANKSHA JAIN
Slide present statistical mining of Malicious-Executable dataset collected from various antivirus log-files and other sources.
Further classifications of malicious code as per their impact on user's system & distinguishes threats on the muse in their connected severity.
Implementation of the algorithms JRIP ,PART and RIDOR in additional economical manner to acquire a level of accuracy to the classification results.
MALWARE DETECTION USING MACHINE LEARNING ALGORITHMS AND REVERSE ENGINEERING O...IJNSA Journal
This research paper is focused on the issue of mobile application malware detection by Reverse Engineering of Android java code and use of Machine Learning algorithms. The malicious software characteristics were identified based on a collected set of total number of 1958 applications (including 996 malware applications). During research a unique set of features was chosen, then three attribute selection algorithms and five classification algorithms (Random Forest, K Nearest Neighbors, SVM, Nave Bayes and Logistic Regression) were examined to choose algorithms that would provide the most effective rate of malware detection.
Android Malware Detection in Official and Third Party Application StoresEswar Publications
Android is one of the most popular operating system for mobile devices and tablets. The growing number of Android users and open source nature of this platform has also attracted attackers to target Android devices. This paper presents the static and dynamic analysis of the Android applications in order to detect malware. In this work, we have performed permission based and behavioural based filtering of Android applications with the help of malware analysis tools. Our results revel that 80% of the applications request for dangerous permissions.
13% applications consist of malicious activities. Most of the applications are interested in the device data like contact lists, IMEI, IMSI, SMS etc. These results clearly indicate the need for better security measures for Android apps.
A FRAMEWORK FOR ANALYSIS AND COMPARISON OF DYNAMIC MALWARE ANALYSIS TOOLSIJNSA Journal
Malware writers have employed various obfuscation and polymorphism techniques to thwart static analysis
approaches and bypassing antivirus tools. Dynamic analysis techniques, however, have essentially
overcome these deceits by observing the actual behaviour of the code execution. In this regard, various
methods, techniques and tools have been proposed. However, because of the diverse concepts and
strategies used in the implementation of these methods and tools, security researchers and malware
analysts find it difficult to select the required optimum tool to investigate the behaviour of a malware and to
contain the associated risk for their study. Focusing on two dynamic analysis techniques: Function Call
monitoring and Information Flow Tracking, this paper presents a comparison framework for dynamic
malware analysis tools. The framework will assist the researchers and analysts to recognize the tool’s
implementation strategy, analysis approach, system-wide analysis support and its overall handling of
binaries, helping them to select a suitable and effective one for their study and analysis.
IJERA (International journal of Engineering Research and Applications) is International online, ... peer reviewed journal. For more detail or submit your article, please visit www.ijera.com
an error in that computer program. In order to improve the software quality, prediction of faulty modules is
necessary. Various Metric suites and techniques are available to predict the modules which are critical and
likely to be fault prone. Genetic Algorithm is a problem solving algorithm. It uses genetics as its model of
problem solving. It’s a search technique to find approximate solutions to optimization and search
problems.Genetic algorithm is applied for solving the problem of faulty module prediction and as well as
for finding the most important attribute for fault occurrence. In order to perform the analysis, performance
validation of the Genetic Algorithm using open source software jEdit is done. The results are measured in
terms Accuracy and Error in predicting by calculating probability of detection and probability of false
Alarms
DETECTION OF MALICIOUS EXECUTABLES USING RULE BASED CLASSIFICATION ALGORITHMSAAKANKSHA JAIN
Slide present statistical mining of Malicious-Executable dataset collected from various antivirus log-files and other sources.
Further classifications of malicious code as per their impact on user's system & distinguishes threats on the muse in their connected severity.
Implementation of the algorithms JRIP ,PART and RIDOR in additional economical manner to acquire a level of accuracy to the classification results.
Android is a Linux based operating system used for smart phone devices. Since 2008, Android devices gained huge market share due to its open architecture and popularity. Increased popularity of the Android devices and associated primary benefits attracted the malware developers. Rate of Android malware applications increased between 2008 and 2016. In this paper, we proposed dynamic malware detection approach for Android applications. In dynamic analysis, system calls are recorded to calculate the density of the system calls. For density calculation, we used two different lengths of system calls that are 3 gram and 5 gram. Furthermore, Naive Bayes algorithm is applied to classify applications as benign or malicious. The proposed algorithm detects malware using 100 real world samples of benign and malware applications. We observe that proposed method gives effective and accurate results. The 3 gram Naive Bayes algorithm detects 84 malware application correctly and 14 benign application incorrectly. The 5 gram Naive Bayes algorithm detects 88 malware application correctly and 10 benign application incorrectly. Mr. Tushar Patil | Prof. Bharti Dhote "Malware Detection in Android Applications" Published in International Journal of Trend in Scientific Research and Development (ijtsrd), ISSN: 2456-6470, Volume-3 | Issue-5 , August 2019, URL: https://www.ijtsrd.com/papers/ijtsrd26449.pdfPaper URL: https://www.ijtsrd.com/engineering/computer-engineering/26449/malware-detection-in-android-applications/mr-tushar-patil
A FRAMEWORK FOR THE DETECTION OF BANKING TROJANS IN ANDROIDIJNSA Journal
Android is the most widely used operating system today and occupies more than 70% share of the smartphone market. It is also a popular target for attackers looking to exploit mobile operating systems for personal gains. More and more malware are targeting android operating system like Android Banking Trojans (ABTs) which are widely being discovered. To detect such malware, we propose a prediction model for ABTs that is based on hybrid analysis. The feature sets used with the machine learning algorithms are permissions, API calls, hidden application icon and device administrator. Feature selection methods based on frequency and gain ratio are used to minimize the number of features as well as to eliminate the low-impact features. The proposed system is able to achieve significant performance with selected machine learning algorithms and achieves accuracy up to 98% using random forest classifier.
ANDROINSPECTOR: A SYSTEM FOR COMPREHENSIVE ANALYSIS OF ANDROID APPLICATIONSIJNSA Journal
Android is an extensively used mobile platform and with evolution it has also witnessed an increased influx of malicious applications in its market place. The availability of multiple sources for downloading applications has also contributed to users falling prey to malicious applications. A major hindrance in blocking the entry of malicious applications into the Android market place is scarcity of effective mechanisms to identify malicious applications. This paper presents AndroInspector, a system for comprehensive analysis of an Android application using both static and dynamic analysis techniques. AndroInspector derives, extracts and analyses crucial features of Android applications using static analysis and subsequently classifies the application using machine learning techniques. Dynamic analysis includes automated execution of Android application to identify a set of pre-defined malicious actions performed by application at run-time.
The International Journal of Engineering & Science is aimed at providing a platform for researchers, engineers, scientists, or educators to publish their original research results, to exchange new ideas, to disseminate information in innovative designs, engineering experiences and technological skills. It is also the Journal's objective to promote engineering and technology education. All papers submitted to the Journal will be blind peer-reviewed. Only original articles will be published.
The papers for publication in The International Journal of Engineering& Science are selected through rigorous peer reviews to ensure originality, timeliness, relevance, and readability.
International Journal of Computational Engineering Research (IJCER) is dedicated to protecting personal information and will make every reasonable effort to handle collected information appropriately. All information collected, as well as related requests, will be handled as carefully and efficiently as possible in accordance with IJCER standards for integrity and objectivity.
Provide security about risk score in mobile application’seSAT Journals
Abstract Now days as the use of mobile devices is increasing rapidly day by day, huge number of mobile apps are coming into the market. These apps ask the user access to various kinds of permissions, and also many of these perform the same task. The user comes at risk with presence of some malicious app due to access of permission it will get, as android provides a stand –alone defense mechanism with respect to malicious apps. Where it warns the user about the permissions the app requires, trusting that the user will make proper decision, which requires the user to have the technical knowledge and time, which is not user friendly for each user. Also classification of these apps can be useful in understanding the user preferences and can motivate the intelligent personalized services. But to effectively classify the app is a nontrivial task as limited contextual information is available. To address these two issues an approach is proposed where the apps will be classified first using the enriched contextual information from web search engine, then with the contextual features from the context-rich device logs of mobile users and calculating the risk score for the app in order to generate a user friendly metric for the user to use when choosing the app. This will help us to get effective classification of the mobile apps and protect the user’s mobile devices from malicious apps. Key Words: - Mobile apps classification, risk, malware, web knowledge, enriched contextual information.
Similar to IRJET- Android Malware Detection using Deep Learning (20)
Understanding Inductive Bias in Machine LearningSUTEJAS
This presentation explores the concept of inductive bias in machine learning. It explains how algorithms come with built-in assumptions and preferences that guide the learning process. You'll learn about the different types of inductive bias and how they can impact the performance and generalizability of machine learning models.
The presentation also covers the positive and negative aspects of inductive bias, along with strategies for mitigating potential drawbacks. We'll explore examples of how bias manifests in algorithms like neural networks and decision trees.
By understanding inductive bias, you can gain valuable insights into how machine learning models work and make informed decisions when building and deploying them.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
Welcome to WIPAC Monthly the magazine brought to you by the LinkedIn Group Water Industry Process Automation & Control.
In this month's edition, along with this month's industry news to celebrate the 13 years since the group was created we have articles including
A case study of the used of Advanced Process Control at the Wastewater Treatment works at Lleida in Spain
A look back on an article on smart wastewater networks in order to see how the industry has measured up in the interim around the adoption of Digital Transformation in the Water Industry.
Saudi Arabia stands as a titan in the global energy landscape, renowned for its abundant oil and gas resources. It's the largest exporter of petroleum and holds some of the world's most significant reserves. Let's delve into the top 10 oil and gas projects shaping Saudi Arabia's energy future in 2024.
Water billing management system project report.pdfKamal Acharya
Our project entitled “Water Billing Management System” aims is to generate Water bill with all the charges and penalty. Manual system that is employed is extremely laborious and quite inadequate. It only makes the process more difficult and hard.
The aim of our project is to develop a system that is meant to partially computerize the work performed in the Water Board like generating monthly Water bill, record of consuming unit of water, store record of the customer and previous unpaid record.
We used HTML/PHP as front end and MYSQL as back end for developing our project. HTML is primarily a visual design environment. We can create a android application by designing the form and that make up the user interface. Adding android application code to the form and the objects such as buttons and text boxes on them and adding any required support code in additional modular.
MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software. It is a stable ,reliable and the powerful solution with the advanced features and advantages which are as follows: Data Security.MySQL is free open source database that facilitates the effective management of the databases by connecting them to the software.
CW RADAR, FMCW RADAR, FMCW ALTIMETER, AND THEIR PARAMETERSveerababupersonal22
It consists of cw radar and fmcw radar ,range measurement,if amplifier and fmcw altimeterThe CW radar operates using continuous wave transmission, while the FMCW radar employs frequency-modulated continuous wave technology. Range measurement is a crucial aspect of radar systems, providing information about the distance to a target. The IF amplifier plays a key role in signal processing, amplifying intermediate frequency signals for further analysis. The FMCW altimeter utilizes frequency-modulated continuous wave technology to accurately measure altitude above a reference point.
We have compiled the most important slides from each speaker's presentation. This year’s compilation, available for free, captures the key insights and contributions shared during the DfMAy 2024 conference.
NO1 Uk best vashikaran specialist in delhi vashikaran baba near me online vas...Amil Baba Dawood bangali
Contact with Dawood Bhai Just call on +92322-6382012 and we'll help you. We'll solve all your problems within 12 to 24 hours and with 101% guarantee and with astrology systematic. If you want to take any personal or professional advice then also you can call us on +92322-6382012 , ONLINE LOVE PROBLEM & Other all types of Daily Life Problem's.Then CALL or WHATSAPP us on +92322-6382012 and Get all these problems solutions here by Amil Baba DAWOOD BANGALI
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Industrial Training at Shahjalal Fertilizer Company Limited (SFCL)MdTanvirMahtab2
This presentation is about the working procedure of Shahjalal Fertilizer Company Limited (SFCL). A Govt. owned Company of Bangladesh Chemical Industries Corporation under Ministry of Industries.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.